Adaptive virtual machine request approver

ABSTRACT

An adaptive request handler (ARH) receives a virtual machine (VM) request from a user and determines whether to automatically approve the VM request using a tolerance that defines an allowable amount of deviation from preset resource specifications. In some embodiments, the ARH adaptively varies the tolerance based on one or more monitored factors, such as an aggregate system resource utilization by and/or a billing history of the user or a group that includes the user. In some embodiments, the VM request is based on a template selected by the user from among a plurality of templates eligible for automatic approval, wherein a plurality of tolerances each defines an allowable amount of deviation from preset resource specifications of a respective one of the eligible templates. The ARH may, in some embodiments, vary each of the plurality of tolerances independently based on one or more monitored factors.

BACKGROUND

The present invention relates in general to the data processing field.More particularly, the present invention relates to a method, apparatusand computer program product for automatically approving virtual machine(VM) requests using a tolerance that defines an allowable amount ofdeviation from preset resource specifications, wherein the tolerance canbe varied adaptively based on one or more monitored factors.

SUMMARY

In accordance with some embodiments of the present invention, anadaptive request handler (ARH) receives a virtual machine (VM) requestfrom a user that modifies preset resource modifications of a template ormodifies current resource specifications of an active VM associated withpreset resource specifications. The ARH determines whether toautomatically approve the VM request using a tolerance that defines anallowable amount of deviation from the preset resource specifications.In some embodiments of the present invention, the ARH adaptively variesthe tolerance based on one or more monitored factors, such as anaggregate system resource utilization by the user or a group thatincludes the user and/or a billing history of the user or a group thatincludes the user. In some embodiments of the present invention, theuser selects a template from among a plurality of templates eligible forautomatic approval, wherein a plurality of tolerances each defines anallowable amount of deviation from preset resource specifications of arespective one of the eligible templates. The ARH may, in someembodiments of the present invention, vary each of the plurality oftolerances independently based on one or more monitored factors.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Embodiments of the present invention will hereinafter be described inconjunction with the appended drawings, where like designations denotelike elements.

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 4 illustrates an example representation of a computer systemconnected to a client computer via a network for automatically approvingVM requests using a tolerance according to an embodiment of the presentinvention.

FIG. 5 illustrates a cloud computing system for automatically approvingVM requests using a tolerance in accordance with an embodiment of thepresent invention.

FIG. 6 is a flow diagram illustrating a method for automaticallyapproving VM requests using a tolerance in accordance with the someembodiments of the present invention.

FIG. 7 is a flow diagram illustrating an exemplary method (correspondingto step 602 shown in FIG. 6) for receiving a VM request from a cloudconsumer in accordance with the some embodiments of the presentinvention.

FIG. 8 is a flow diagram illustrating an exemplary method (correspondingto step 604 shown in FIG. 6) for determining whether to automaticallyapprove a VM request using a tolerance in accordance with the someembodiments of the present invention.

FIG. 9 is a flow diagram illustrating a method for defining a toleranceand adaptively varying the tolerance over time in accordance with thesome embodiments of the present invention.

FIG. 10 is a flow diagram illustrating an exemplary method(corresponding to step 902 shown in FIG. 9) for receiving a tolerancedefined by an IT administrator for each template selected as eligiblefor automatic approval in accordance with the some embodiments of thepresent invention.

FIG. 11 is a flow diagram illustrating an exemplary method(corresponding to step 904 shown in FIG. 9) for adaptively varying thetolerance for each eligible template based on one or more monitoredfactors in accordance with the some embodiments of the presentinvention.

DETAILED DESCRIPTION

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide).

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and mobile desktop.

New cloud technology, such as IBM SmartCloud Entry, allows users(including non-IT users) to deploy and modify their own virtual machines(VMs) quickly and easily. An approval system is setup whereby requestsfor new or changed VMs are sent to the IT administrator for approval.Requests for new or changed VMs are referred to as “VM requests”. EachVM request is typically based on a defined image (e.g., a “template”).This manual approval process affords the IT administrator some controlon the number of VMs deployed and the amount of resources used thereby.Unfortunately, in a large environment, with thousands of defined images(templates) and many thousands of users, the number of VM requests canquickly become overwhelming, resulting in the IT administrator becominga bottleneck for approval of VM requests.

In accordance with some embodiments of the present invention, a decisionof whether or not to approve VM request is made automatically withoutrequiring the approval of VM requests by the IT administrator. Instead,the decision is made automatically using a tolerance that defines anallowable amount of deviation from preset resource specifications of atemplate or an active VM, wherein the tolerance can be varied adaptivelybased on one or more monitored factors. No longer does the ITadministrator become a bottleneck for approval of VM requests.Accordingly, in accordance with some embodiments of the presentinvention, the waiting times experienced by users for VM deployment canbe significantly reduced.

FIG. 4 illustrates an example representation of a computer system 400connected to one or more client computers 460 via a network 455,according to some embodiments. For the purposes of this disclosure,computer system 400 may represent practically any type of computer,computer system, or other programmable electronic device, including butnot limited to, a client computer, a server computer, a portablecomputer, a handheld computer, an embedded controller, etc. In someembodiments, computer system 400 may be implemented using one or morenetworked computers, e.g., in a cluster or other distributed computingsystem.

The computer system 400 may include, without limitation, one or moreprocessors (CPUs) 405, a network interface 415, an interconnect 420, amemory 425, and a storage 430. The computer system 400 may also includean I/O device interface 410 used to connect I/O devices 412, e.g.,keyboard, display, and mouse devices, to the computer system 400.

Each processor 405 may retrieve and execute programming instructionsstored in the memory 425 or storage 430. Similarly, the processor 405may store and retrieve application data residing in the memory 425. Theinterconnect 420 may transmit programming instructions and applicationdata between each processor 405, I/O device interface 410, networkinterface 415, memory 425, and storage 430. The interconnect 420 may beone or more busses. The processor 405 may be a single central processingunit (CPU), multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 405 may bea digital signal processor (DSP).

The memory 425 may be representative of a random access memory, e.g.,Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM),read-only memory, or flash memory. The storage 430 may be representativeof a non-volatile memory, such as a hard disk drive, solid state device(SSD), or removable memory cards, optical storage, flash memory devices,network attached storage (NAS), or connections to storage area network(SAN) devices, or other devices that may store non-volatile data. Thenetwork interface 415 may be configured to transmit data via thecommunications network 455.

The memory 425 may include an adaptive request handler (ARH) 435, one ormore received VM requests 440, and template/tolerance data 445. Althoughthese elements are illustrated as residing in the memory 425, any of theelements, or combinations thereof, may reside in the storage 430 orpartially in the memory 425 and partially in the storage 430. The ARH435 has a set (at least one) of program modules that generally carry outthe functions and/or methodologies of embodiments of the invention asdescribed herein.

The network 455 may be any suitable network or combination of networksand may support any appropriate protocol suitable for communication ofdata and/or code to/from the server computer system 400 and the clientcomputer system 460. In some embodiments, the network 455 may supportwireless communications. In other embodiments, the network 455 maysupport hardwired communications. The network 455 may be the Internetand may support Internet Protocol in some embodiments. In otherembodiments, the network 455 may be implemented as a local area network(LAN) or a wide area network (WAN). The network 455 may also beimplemented as a cellular data network. Although the network 455 isshown as a single network in the figures, one or more networks of thesame or different types may be included.

The client computer system 460 may include some or all of the hardwareand software elements of the computer system 400 previously described.As shown, there may be one or more client computers 460 connected to thecomputer system 400 via the network 455. In some embodiments, one ormore client computers 460 may send the VM request 440 by network 455 tocomputer system 400.

FIG. 5 illustrates a cloud computing system 500 for automaticallyapproving virtual machine (VM) requests using a tolerance in accordancewith an embodiment of the present invention.

The cloud computing system 500 comprises a cloud manager 510, a virtualmachine manager (VMM) 520, one or more physical servers 530 provisionedinto one or more virtual machines (VM) 532, one or more cloud consumers540, an IT administrator 542, a template repository 550, a requesthistory database 552, a billing history database 554, an assetmanagement database (AMDB) 556, and a configuration management database(CMDB) 558. The cloud manager 510, the VMM 520, the template repository550, the request history database 552, the billing history database 554,the AMDB 556, the CMDB 558 run on one or more respective conventionalcomputer system/sever. See, for example, descriptions supra of thecomputer system/server 12 (shown in FIG. 1) and the server computersystem 400 (shown in FIG. 4) for details of a conventional computersystem.

The cloud manager 510, working in conjunction with virtual machinemanager (VMM) 520, manages the virtual machines 532 of the cloudcomputing system 500. As is conventional, the cloud manager 510 may, forexample, optimize the workload distribution among physical servers 530in accordance with workload balancing policies set by the ITadministrator 542. For example, the cloud manager 510 may includeconventional components such as the IBM SmartCloud Entry, the IBMSystems Director, the IBM Flex System Manager, the IBM VMControl, andVirtual I/O Server (VIOS).

The cloud manager 510, in accordance with some embodiments of thepresent invention, also includes an adaptive request handler (ARH) 512.The ARH 512 shown in FIG. 5 may, for example, correspond to the adaptiverequest handler 435 shown in FIG. 4. The ARH 512, which may be stored inmemory, has a set (at least one) of program modules that generally carryout the functions and/or methodologies of embodiments of the inventionas described herein.

The ARH 512 receives from the cloud consumer 540 a virtual machine (VM)request based on a template, in accordance with some embodiments of thepresent invention, and determines whether to automatically approve theVM request using a tolerance that defines an allowable amount ofdeviation from preset resource specifications of the template. Inaccordance with some embodiments of the present invention, the ARH 512adaptively varies the tolerance based on one or more monitored factors,such as an aggregate system resource utilization by the cloud consumer540 or a group that includes the cloud consumer 540, or an unpaidbalance reflected in a billing history of the cloud consumer 540 or agroup that includes the cloud consumer 540. For example, the ARH 512 mayadaptively vary the tolerance by using information accessed from variousdatabases (e.g., the temple repository 550, the request history database552, the billing history database 554, the asset management database(AMDB) 556, and the configuration management database (CMDB) 558).

The virtual machine manager (VMM) 520 monitors and manages the physicalservers 530 and the virtual machines (VMs) 532 running on the physicalservers 530. For example, the cloud manager 510 receives performancedata of the cloud computing system 500 from the VMM 520. As isconventional, the cloud manager 510 automatically controls theallocation of the VMs 532 over the physical servers 530 by use of theVMM 520, communicating via, inter alia, hypervisors, VMM applicationprogramming interfaces (APIs), and the like. The VMM 520 receivesrequests for virtual operations and physical operations from the cloudmanager 510 and performs adding, moving, deleting, suspending virtualmachines to and from respective physical machines, changingconfiguration of virtual machine assignment for respective physicalservers. Examples of VMM 520 may be, inter alia, the IBM Power SystemsHardware Management Console (HMC) or the IBM Integrated VirtualizationManager (IVM). In some embodiments of the present invention, the VMM 520and the cloud manager 510 may be combined into one software module.

Each of the physical servers 530 is a conventional computersystem/server that runs zero, one or more virtual machines 532. As isconventional, the virtual machines 532 may be deployed using templates.A template is an image of a virtual machine that is used to create a newvirtual machine. Each template defines a virtual machine in terms ofresource specifications (e.g., memory and CPU settings, and VMexpiration period). The resource specifications of a template are alsoreferred to as the template's “configuration parameters”.

The cloud consumer 540, in accordance with some embodiments of thepresent invention, is a human user requesting deployment and/orrequesting modification of a virtual machine 532 by interacting with thecloud manager 510. In accordance with other embodiments of the presentinvention, the cloud consumer 540 is a software program or automatedprocess. In accordance with some embodiments of the present invention,the cloud consumer 540 interacts with the cloud manager 510 using aself-service web portal to send a virtual machine (VM) request based ona template to the ARH 512 of the cloud manager 510. The cloud consumer540 selects the template from a list of eligible templates, i.e.,templates eligible for automatic approval. Each eligible template haspreset resource specifications that may be modified by the cloudconsumer 540. The list of eligible templates is essentially an “imagecatalog” of appliances that are eligible for automatic approval. Ingeneral, the term “image catalog” describes a list of appliances thatusers can provision.

Once the ARH 512 receives the VM request from cloud consumer 540, theARH 512 determines whether or not to automatically approve the VMrequest. The ARH 512 also adds the VM request to the request historydatabase 552.

The IT administrator 542 is a human user configuring and monitoringoperations of the cloud computing system 500 by interacting with thecloud manager 510. As is conventional, the IT administrator 542 may setpolicies controlling workload balancing, defines costs for operating thecloud computing system 500, and creates, deletes, and reconfiguresvirtual machines in respective virtual servers. As is also conventional,the IT administrator 542 can configure the parameters of an applianceand place them in the appropriate project (grouping) for assigningaccess. An appliance, which is used to create new virtual machines, is amaster disk image of a virtual machine and its associated configurationparameters. Workloads and appliances may be grouped in projects by theIT administrator 542.

In accordance with some embodiments of the present invention, the ITadministrator 542 also interacts with the cloud manager 510 using acloud administrator web portal to select templates eligible forautomatic approval, set resource specifications for eligible templates,and define a tolerance for each eligible template. Also in accordancewith some embodiments of the present invention, the IT administrator 542may manually approve, modify or deny VM requests from the cloud consumer540 that are not automatically approved (or are provisionally approved)by the ARH 512 of the cloud manager 510.

The template repository 550 stores template/tolerance data including,but not limited to, data regarding the templates and data regarding theeligible templates and the tolerances. The data regarding the templatesmay, for example, include a list of the templates along with any presetresource specifications for each template. Each eligible template isstored in the template repository 550 in such a manner as to identifythe preset resource specifications and the tolerance associatedtherewith. The data regarding the eligible templates and the tolerancesmay, for example, include a list of the eligible templates, the presetresource specifications for each eligible template, and the tolerancefor each eligible template.

In some embodiments of the present invention, the template/tolerancedata stored in the template repository 550 may include zero, one or moreadjusted tolerances for each eligible template in addition to, or inlieu of, the original tolerance for each eligible template. The dataregarding eligible templates and the tolerances may, for example,include a list of the eligible templates, the preset resourcespecifications for each eligible template, the original tolerance foreach eligible template, and zero, one or more adjusted tolerances foreach eligible template. For example, two different adjusted tolerancesmay be each associated with the same eligible template for two differentcloud consumers, respectively. In such an example, an eligible templatemay have an original tolerance for a first cloud consumer (or a groupthat includes the first cloud consumer), a first adjusted tolerance fora second cloud consumer (or a group that includes the second cloudconsumer), and a second adjusted tolerance for a third cloud consumer(or a group that includes the third cloud consumer).

The request history database 552 stores historical data regarding whattemplates have been requested by what cloud consumer. This historicaldata may include, but are not limited to, the Customer ID of the cloudconsumer that sent the VM request, the template identified in the VMrequest, any modification to the preset resource specifications for thetemplate identified in the VM request, the date and time when the VMrequest was received, and the disposition of the VM request (e.g.,whether the VM request was approved, denied, or modified by an ITadministrator and, if modified, how the VM request was modified by theIT administrator).

The billing history database 554 stores billing history data regardingthe billing history for all accounts. Any user (e.g., cloud consumersand/or groups that include cloud consumers) who can log in to the cloudmanager can be assigned an account. The workloads that are owned by thisuser are charged to that account. A user can be a member of only oneaccount at a time. The account is typically identified by a Customer ID,i.e., the name of the owner of the workloads. For each account, thebilling history data includes, but is not limited to, the Customer IDand an unpaid balance. The billing history data may also include, foreach cloud consumer, an unpaid balance limit that defines a limit withrespect to the unpaid balance for the cloud consumer. For example, insome embodiments of the present invention, if the unpaid balance of acloud consumer is greater than the unpaid balance limit, the toleranceis adjusted to be more restrictive for the cloud consumer.

The asset management database (AMDB) 556 stores software assetinformation regarding a number of available software licenses, a numberof installed software licenses, and the like.

The configuration management database (CMDB) 558 stores informationabout dependencies between the physical servers 530 and the virtualmachines 532 running on the respective physical servers 530 and topologyinformation as the virtual machine assignment for respective physicalservers. As is conventional, the cloud manager 510 may update the CMDB558 with new topology information resulting from optimization ofworkload distribution among physical servers. The CMDB 558 may alsostore information about constraints of the cloud computing system 500,such as hardware resources of the respective physical servers 530available for virtual machine assignment.

FIG. 6 is a flow diagram illustrating a method for automaticallyapproving VM requests using a tolerance in accordance with the someembodiments of the present invention. In the method 600, the stepsdiscussed below (steps 602-604) are performed. The method 600 begins asthe adaptive request handler (ARH) receives a VM request from a cloudconsumer (step 602). An exemplary method of implementing step 602 isshown FIG. 7, described below.

Then, the method 600 continues as the ARH determines whether toautomatically approve the VM request (received in step 602) using atolerance (step 604). An exemplary method of implementing step 604 isshown in FIG. 8, described below.

Hence, in accordance with some embodiments of the present invention, thewaiting times experienced by cloud consumers for VM provisioning can bereduced because the decision whether or not to approve VM request ismade automatically without requiring the approval of VM requests by theIT administrator. No longer does the IT administrator become abottleneck for approval of VM requests.

FIG. 7 is a flow diagram illustrating an exemplary method 700 forreceiving a VM request from a cloud consumer (step 602 shown in FIG. 6)in accordance with the some embodiments of the present invention. In themethod 700, the steps discussed below (steps 702-704) are performed. Theexemplary method 700 utilizes a self-service web portal feature of thecloud manager. The cloud consumer interacts with a virtual serverrunning the cloud manager using a self-service web portal on the cloudconsumer's computer. The self-service web portal may, for example, bedownloaded to a web browser on the cloud consumer's computer in responseto the cloud consumer having clicked a “request a VM” button on awebpage that the cloud consumer navigated to by drilling down in acompany's website. One skilled in the art will appreciate, however, thatany number of other methods for receiving a VM request from a cloudconsumer may be used in lieu of the exemplary self-service web portal.

The method 700 begins as a cloud consumer logs into the cloud managerfrom the cloud consumer's computer using a self-service web portal (step702). This step is conventional. For example, conventional cloudmanagers such as the IBM SmartCloud Entry feature a self-service webportal that requires the cloud consumer to log into the cloud managerfrom the cloud consumer's computer.

Then, the method 700 continues as the cloud consumer requests a VM usingthe self-service web portal by selecting from a list of availabletemplates with preset resource specifications (e.g., memory and CPUsettings, and VM expiration period) and/or modifies the selectedtemplate using the self-service web portal by inputting modifications tothe preset resource specifications, or modifies current resourcespecifications of a live (active) VM associated with preset resourcespecifications (step 704). This step is conventional. For example,conventional cloud managers such as the IBM SmartCloud Entry feature aself-service web portal that enables a cloud consumer to request a VM byselecting from a list of available templates with preset resourcespecifications and/or modify the selected template by inputtingmodifications to the preset resource specifications.

In step 704, the cloud consumer's computer generates a “VM request”based on the information input by the cloud consumer (e.g., the selectedtemplate and any modification to preset resource specifications of thetemplate, or a modification to current resource specifications of anactive VM associated with preset resource specifications), and sends theVM request to the adaptive request handler (ARH) of the cloud manager.The VM request may, for example, be generated and sent in response tothe cloud consumer having clicked a “deploy” button on the self-serviceweb portal. In general, the VM request is generated by the cloudconsumer's computer based on the information input by the cloudconsumer, such as the information the cloud consumer provided via theself-service web portal and/or information the cloud consumer or thecloud consumer's computer has provided via some other mechanism.

The VM request generally includes numerous data elements, such asCustomer ID, VM Type, Request Start Time, Request End Time, etc. One ormore of these data elements identifies the template selected by thecloud consumer and any modification to the preset resourcespecifications input by the cloud consumer. One skilled in the art willappreciate that any type of data file may be generated and sent in step704 as the “VM request”.

FIG. 8 is a flow diagram illustrating an exemplary method 800 fordetermining whether to automatically approve a VM request using atolerance (step 604 shown in FIG. 6) in accordance with the someembodiments of the present invention. The method 800 begins as theadaptive request handler (ARH) determines whether VM request receivedfrom a cloud consumer's computer is a template eligible for automaticapproval (step 802). The ARH may, for example, parse the VM request intodata elements, access the template repository, and compare the parseddata element that identifies the template selected by the cloud consumeragainst the eligible templates stored in the template repository. Asdescribed below, with reference FIG. 10, eligible templates aretemplates that were (previously) selected by an IT administrator aseligible for automatic approval and stored as template/tolerance data inthe template repository. If the VM request is not eligible for automaticapproval (step 802=No), the method 800 continues by the ARH invoking themanual approval process (step 804). The manual approval process isinvoked to require manual approval of the VM request by an ITadministrator before the VM can be deployed. Under the manual approvalprocess, the IT administrator can either manually approve the VM request“as-is”, or manually adjust or reject the VM request.

If the VM request is eligible for automatic approval (step 802=Yes), themethod 800 continues as the ARH determines whether the VM request iswithin a tolerance associated with the selected template (step 806). Thetolerance associated with the selected template defines an allowableamount of deviation from preset resource specifications of the selectedtemplate. The ARH may, for example, parse the VM request into dataelements, access the template repository, and compare the parsed dataelement that identifies any modification to the preset resourcespecifications input by the cloud consumer against the toleranceassociated with the selected template stored in the template repository.As described below, with reference to FIG. 10, a tolerance was(previously) defined by an IT administrator for each eligible templateand stored as template/tolerance data in the template repository. Forexample, the tolerance associated with the selected template may havebeen defined by the IT administrator to be twice the selected template'spreset resource specifications.

If the VM request is within the tolerance associated with the selectedtemplate (step 806=Yes), the method 800 continues as the ARHautomatically approves the VM request (step 808). The ARH may, forexample, send a request to virtual machine manager (VMM), and theconventional deployment process begins. In accordance with someembodiments of the present invention, in step 808, the ARH mayautomatically approve the VM request on a provisional basis, i.e.,subject to a subsequent manual review by an IT administrator.

If the VM request is outside the tolerance associated with the selectedtemplate (step 806=No), the method 800 continues as the ARHautomatically denying the VM request (step 810). The ARH may, forexample, send a message to the cloud consumer's computer informing thecloud consumer that the VM request has been denied. Step 810 may, inaccordance with some embodiments of the present invention, automaticallydeny the VM request on a provisional basis, and then the method 800would flow to step 804 to invoke a manual approval process.

For example, the tolerance may be defined to be twice the size of thetemplate's preset resource specifications (also referred to herein asthe “default image”). Hence, if the default image has 2 CPUs and 2 GB ofRAM and the tolerance is defined as twice the default image, forexample, a VM request will be automatically approved as long as the VMrequest has no more than 4 CPUs and 4 GB of RAM (i.e., 2× the template'spreset resource specifications). Conversely, for the same default imageand the same tolerance, a VM request will be automatically rejected ifthe VM request has more than 4 CPUs or more than 4 GBs of RAM (e.g., ifa typo with respect to the CPUs needed requests 20,000 CPUs).

In the method 800, the ARH determines whether to automatically approve aVM request using a tolerance that is associated with an eligibletemplate (i.e., a template that is eligible for automatic approval). Oneskilled in the art will appreciate, however, that the method 900 is alsoapplicable to determining whether to automatically approve a VM requestusing a tolerance that is associated with an eligible live (active) VM(i.e., a live VM that is eligible for automatic approval).

FIG. 9 is a flow diagram illustrating a method 900 for defining atolerance and adaptively varying the tolerance over time in accordancewith the some embodiments of the present invention. In the method 900,the steps discussed below (steps 902-904) are performed. The method 900begins as the adaptive request handler (ARH) receives a tolerancedefined by an IT administrator for each template selected by the ITadministrator as eligible for automatic approval (step 902). Anexemplary method of implementing step 902 is shown FIG. 10, describedbelow.

Then, the method 900 continues as the ARH adaptively varies thetolerance for each eligible template (i.e., each template selected aseligible for automatic approval) based on one or more monitored factors(step 904). An exemplary method of implementing step 904 is shown inFIG. 11, described below.

In the method 900, a tolerance is defined and adaptively varied for eachof one or more eligible templates (i.e., templates that are eligible forautomatic approval). One skilled in the art will appreciate, however,that the method 900 is also applicable to defining a tolerance andadaptively varying the tolerance for each of one or more eligible liveVMs (i.e., live VMs that are eligible for automatic approval).

FIG. 10 is a flow diagram illustrating an exemplary method 1000 forreceiving a tolerance defined by an IT administrator for each templateselected as eligible for automatic approval (step 902 shown in FIG. 9)in accordance with the some embodiments of the present invention. Themethod 1000 utilizes a cloud administrator web portal feature of thecloud manager. The IT administrator interacts with a virtual serverrunning the cloud manager using a cloud administrator web portal on theIT administrator's computer. One skilled in the art will appreciate,however, that any number of other methods for receiving a tolerancedefined by an IT administrator for each template selected as eligiblefor automatic approval may be used in lieu of the exemplary cloudadministrator web portal.

The method 1000 begins as an IT administrator logs into the cloudmanager from the IT administrator's computer using a cloud administratorweb portal (step 1002). This step is conventional. For example,conventional cloud managers such as the IBM SmartCloud Entry feature acloud administrator web portal that requires the IT administrator to loginto the cloud manager from the IT administrator's computer.

Then, the method 1000 continues as the IT administrator selects one ormore templates as eligible for automatic approval using the cloudadministrator web portal (step 1004). For example, a cloud administratorweb portal of a conventional cloud manager such as the IBM SmartCloudEntry may be enhanced to enable an IT administrator to select one ormore templates as eligible for automatic approval. The cloudadministrator web portal may, for example, display a list of availabletemplates with preset resource specifications in step 1004 from whichthe IT administrator selects one or more eligible templates. Thetemplates selected by the IT administrator will be eligible forautomatic approval.

The cloud administrator web portal may, in another example, display alist of available templates with configurable resource specifications(i.e., configurable by the IT administrator to generate preset resourcespecifications) in step 1004 from which the IT administrator selects oneor more eligible templates and configures the resource specifications ofeach of the one or more eligible templates to generate preset resourcespecifications. The templates selected and configured by the ITadministrator will be eligible for automatic approval.

The method 1000 then continues as the IT administrator defines atolerance for each eligible template using the cloud administrator webportal (step 1006). For example, a cloud administrator web portal of aconventional cloud manager such as the IBM SmartCloud Entry may beenhanced to enable an IT administrator to define a tolerance for each ofthe one or more templates selected as eligible for automatic approval.The cloud administrator web portal may, for example, display a list ofeligible templates with preset resource specifications and prompt the ITadministrator to input a tolerance for each eligible template as amultiple (e.g., 2X) of the preset resource specifications. For example,the IT administrator may input a tolerance for some eligible templatesthat is relatively less restrictive (e.g., twice those particulareligible templates' preset resource specifications) and input atolerance for other eligible templates that is relatively morerestrictive (e.g., 1.1 times those particular eligible templates' presetresource specifications). This example is set forth for purposes ofillustration, not limitation.

One skilled in the art will appreciate the tolerance for each eligibletemplate may be defined in a myriad of ways. The tolerance for aneligible template may, in another example, be defined by the ITadministrator using different multiples for different resourcecomponents of a particular eligible template's preset resourcespecifications (e.g., 1.5 times the CPU setting component of aparticular eligible template's preset resource specifications, 2 timesthe memory setting component of that same eligible template's presetresource specifications, and 1.2 times the VM expiration period settingcomponent of that same eligible template's preset resourcespecifications). In yet another example, a single tolerance may bedefined by the IT administrator globally so that all eligible templateshave the same tolerance.

Then, the method 1000 continues as the ARH generates template/tolerancedata based on the eligible templates selected by the IT administrationand the tolerance defined by the IT administrator for each eligibletemplate, and sends the template/tolerance data to the templaterepository (step 1008). The template/tolerance data associates eacheligible template with a tolerance (and/or, optionally, with one or moreadjusted tolerances). The template/tolerance data may, for example, begenerated and sent in response to the IT administrator having clicked a“save” button on the cloud administrator web portal. One skilled in theart will appreciate that any type of data file may be generated and sentin step 1008 as the “template/tolerance data”.

In the method 1000, a tolerance is defined for each of one or moreeligible templates (i.e., templates that are eligible for automaticapproval). One skilled in the art will appreciate, however, that themethod 1000 is also applicable to defining a tolerance the tolerance foreach of one or more eligible live VMs (i.e., live VMs that are eligiblefor automatic approval).

FIG. 11 is a flow diagram illustrating an exemplary method 1100 foradaptively varying the tolerance for each eligible template based on oneor more monitored factors (step 904 shown in FIG. 9) in accordance withthe some embodiments of the present invention. The method 1100 begins asthe ARH monitors one or more factors (step 1102). For example, the ARHmay monitor an aggregate system resource utilization by the cloudconsumer (or a group that includes the cloud consumer). In anotherexample, the ARH may monitor an unpaid balance reflected in a billinghistory of the cloud consumer (or a group that includes the cloudconsumer).

Then, the method 1100 continues as the ARH determines whether the one ormore monitored factors is within a limit (step 1104). For example, theARH may determine whether the monitored aggregate system resourceutilization by the cloud consumer (or a group that includes the cloudconsumer) is within a limit for aggregate system resource utilization.In another example, the ARH may determine whether the monitored unpaidbalance reflected in the billing history of the cloud consumer (or agroup that includes the cloud consumer) is within an unpaid balancelimit.

If each of the one or more monitored factors is within the limit (step1104=Yes), the method 1100 returns to step 1102 wherein the ARHcontinues monitoring to one or more factors.

If one or more monitored factors is outside the limit (step 1104=No),the method 1100 continues as the ARH adjusts the tolerance (step 1106).For example, the ARH may adjust the tolerance to make it morerestrictive if the monitored aggregate system resource utilization bythe cloud consumer (or a group that includes the cloud consumer) exceedsthe limit for aggregate system resource utilization. In another example,the ARH may adjust the tolerance to make it more restrictive if themonitored unpaid balance in the billing history of the cloud consumer(or a group that includes the cloud consumer) exceeds the unpaid balancelimit.

After the ARH adjusts the tolerance in step 1106, the method 1100returns to step 1102 wherein the ARH continues to monitor one or morefactors. In accordance with some embodiments of the present invention,the ARH may adjust the tolerance repeatedly by a fixed adjustment amount(e.g., 0.1). For example, the ARH may increase the tolerance (i.e., makethe tolerance less restrictive) by an adjustment amount (e.g., 0.1)after every successful manual approved request and/or the ARH maydecrease the tolerance (i.e., make the tolerance more restrictive) by anadjustment amount (e.g., 0.1) after every rejected or manuallyoverridden request. In accordance with other embodiments of the presentinvention, the ARH may adjust the tolerance repeatedly by adjustmentamounts that grow logarithmically or exponentially.

In the method 1100, a tolerance is adaptively varied for each of one ormore eligible templates (i.e., templates that are eligible for automaticapproval). One skilled in the art will appreciate, however, that themethod 1100 is also applicable to adaptively varying a tolerance foreach of one or more eligible live VMs (i.e., live VMs that are eligiblefor automatic approval).

In accordance with some embodiments of the present invention, thetolerance T is automatically adjusted under several different scenariosdescribed below. The exemplary scenarios described below employ variousmonitored factors. The particular monitored factors set forth in theexemplary scenarios below are for purposes of illustration, notlimitation.

Exemplary scenarios where the tolerance T becomes more restrictive: Thetolerance associated with a given template may be adjusted to becomemore restrictive by reducing the multiplier used to define the tolerancein terms of that template's preset resource specifications. For example,an original tolerance of 2.0 times the template's preset resourcespecifications may be adjusted down by an adjustment amount of 0.1 timesthe template's resource specifications so as to become an adjustedtolerance of 1.9 times the template's present resource specifications.In the above example, the multipliers and the adjustment amount are forpurposes of illustration, not limitation. Typically, for a giventemplate, the adjusted tolerance is greater than or equal to 1.0 timesthe template's preset resource specifications. However, the adjustedtolerance, at least in some scenarios, can go less than 1.0 times thetemplate's preset resource specifications. In an exemplarymore-restrictive T scenario, a user has more VMs running (e.g., a userhas 100 VMs continuing to run over the last few months), T may beadjusted to go down by an adjustment amount (e.g., 0.1 times thetemplate's resource specifications) for every VM that user has runningor requests. In another exemplary more-restrictive T scenario, a userapproaches an aggregate quota of resources that user is allowed toconsume (as a group or as an individual), T may be adjusted to go downby an adjustment amount (e.g., 0.1 times the template's resourcespecifications) for every VM that user (as a group or as an individual)has running or requests. In yet another exemplary more-restrictive Tscenario, the cloud runs low on hardware resources, T may be adjusted togo down by an adjustment amount (e.g., 0.1 times the template's resourcespecifications) for every VM any user has running or requests. In stillanother exemplary more-restrictive T scenario, a large number ofrequests occur at once (e.g., 100 VM requests come in at once), T may beadjusted to go down by an adjustment amount (e.g., 0.1 times thetemplate's resource specifications) for every VM any user has running orrequests.

Exemplary scenarios where the tolerance T becomes less restrictive: Thetolerance associated with a given template may be adjusted to becomeless restrictive by increasing the multiplier used to define thetolerance in terms of that template's preset resource specifications.For example, an original tolerance of 2.0 times the template's presetresource specifications may be adjusted up by an adjustment amount of0.1 times the template's resource specifications so as to become anadjusted tolerance of 2.1 times the template's present resourcespecifications. In the above example, the multipliers and the adjustmentamount are for purposes of illustration, not limitation. In an exemplaryless-restrictive T scenario, T is adjusted to become less restrictivefor a user (as a group or as an individual) has a long history of havingall or most requests approved. For example, over time, the IT test groupof an enterprise may have a less restrictive T value than the humanresources group of the enterprise.

Exemplary scenarios where the tolerance T becomes more or lessrestrictive based on the history of previous requests: The history ofprevious requests may be used to guide the training of a toleranceadjusting algorithm. The adaptive request handler may, for example,include a tolerance adjusting algorithm that learns to adjust T up anddown, selectively, by watching manual approval decisions. In anexemplary scenario, user Joe always requests 10 CPUs for a particular VM(e.g., VM_(—)1234) compared to the default 2 CPUs (i.e., the CPU settingof the preset resource specifications of the template that definesVM_(—)1234), and the IT administrator always approves. Over time, T isadjusted to become less restrictive for this particular VM (i.e.,VM_(—)1234) at first just for user Joe, but then T is adjusted to becomeless restrictive for this particular VM (i.e., VM_(—)1234) for all otherusers, as well. as the IT administrator approves a few other users.Subsequently, user Steve repeatedly requests 10 CPUs for this particularVM (i.e., VM_(—)1234), but the IT administrator always denies therequests (e.g., after the requests are automatically approved on aprovisional basis, as described below). Over time, T is adjusted tobecome more restrictive for this particular VM (i.e., VM_(—)1234) foruser Steve.

Exemplary scenarios where the tolerance T becomes more or lessrestrictive based on costs: T can grow at a slower/faster rate dependingon the costs associated with using a VM. Different cloud stacks, as wellas images on those stacks, have different pricing for the licenses,hardware (CPU, RAM, storage), etc. In an exemplary scenario, T can beadjusted up and down, selectively, based on the current image or bill,the aggregate price of images of bills, or team/project aggregate costs.

Exemplary scenarios where the tolerance T becomes more or lessrestrictive based on historic demands: T can take into account historicdemands. In an exemplary scenario, resource and billing histories mayshow that the last day of the month is a high demand day. Accordingly,on the last day of the month, T is automatically adjusted to become morerestrictive so that only preapproved request sizes are automaticallyallowed, and any deviations must go through a manual approval process(IT administrator). On the other hand, resource and billing historiesmay show that mid-month is a low demand period. Accordingly, duringmid-month, T is automatically adjusted to become less restrictive sothat automatic approvals are more likely. The resource and billinghistories and graphs easily show when the demand is high and T is easilyautomatically adjusted based on that history.

Thus, the exemplary scenarios described above employ a myriad ofmonitored factors on which to base the adjustment of the tolerance. Ingeneral, T may be adjusted based on any suitable monitored factor(s).Suitable monitored factors include, but are not limited to, the user,the particular or type of VM image, the group or project the user is amember of, the quotas they (i.e., the user, group or project) belong to,the remaining system resources, the history of previous requests, thehistory of previous bills, the history of previous demands, and thehistory of past manual approvals.

In accordance with some embodiments of the present invention, as someboundary is approached, T is decreased (i.e., T is made morerestrictive) by an adjustment amount (e.g., 0.01) for every active VM ofa user or group. For example, the boundary that is being approached maybe a quota of resources for the user or group. In accordance with otherembodiments of the present invention, as some boundary is approached, Tis decreased (i.e., T is made more restrictive) by an adjustment amount(e.g., 0.01) for every VM (globally for all users). For example, theboundary that is being approached may be a quota of resources for thecloud computing system.

In accordance with some embodiments of the present invention, T isincreased (i.e., T is made less restrictive) and/or T is decreased(i.e., T is made more restrictive) incrementally by a fixed adjustmentamount (e.g., 0.01). In accordance with other embodiments of the presentinvention, T is increased (i.e., T is made less restrictive) and/ordecreased (i.e., T is made more restrictive) incrementally by adjustmentamounts that grow logarithmically or exponentially.

In accordance with some embodiments to the present invention, T isincreased (i.e., T is made less restrictive) by an adjustment amount(e.g., 0.1) after every successful manual approved request and/or T isdecreased (i.e., T is made more restrictive) by an adjustment amount(e.g., 0.1) after every rejected or manually overridden request. Amanually overridden request may have requested 4 CPUs, for example, butthe IT administrator overrides the request by changing the CPU settingto 3 CPUs and then approves.

In accordance with some embodiments of the present invention, anautomatic approval that is a borderline case may be given a status ofApproved Pending Further Review (or similar). Thus, the request isprovisionally approved and the user may start using their VM, but therequest is held and given a special status of Approved Pending FurtherReview so that the request is held pending a human IT administrativereview. The human IT administrator then has the option of agreeing withthe approval, modifying or altering the running VM, or in the extremecase, disallowing the request and deleting the running VM immediately orafter some timeframe.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

One skilled in the art will appreciate that many variations are possiblewithin the scope of the present invention. Thus, while the presentinvention has been particularly shown and described with reference topreferred embodiments thereof, it will be understood by those skilled inthe art that these and other changes in form and details may be madetherein without departing from the spirit and scope of the presentinvention.

1. A computer-implemented method comprising: receiving a virtual machine(VM) request from a user, wherein the VM request modifies presetresource modifications of a template or modifies current resourcespecifications of an active VM associated with preset resourcespecifications; determining whether to automatically approve the VMrequest using a tolerance that defines an allowable amount of deviationfrom the preset resource specifications.
 2. The computer-implementedmethod as recited in claim 1, further comprising: adaptively varying thetolerance based on one or more monitored factors.
 3. Thecomputer-implemented method as recited in claim 2, wherein the one ormore monitored factors include an aggregate system resource utilizationby the user or a group that includes the user.
 4. Thecomputer-implemented method as recited in claim 2, wherein the one ormore monitored factors include a billing history of the user or a groupthat includes the user.
 5. The computer-implemented method as recited inclaim 1, further comprising receiving a tolerance defined by an ITadministrator for each template selected by the IT administrator aseligible for automatic approval.
 6. The computer-implemented method asrecited in claim 1, wherein the user selects the template from among aplurality of templates.
 7. The computer-implemented method as recited inclaim 6, wherein a plurality of tolerances each defines an allowableamount of deviation from preset resource specifications of a respectiveone of the plurality of templates.
 8. The computer-implemented method asrecited in claim 7, further comprising: adaptively varying each of theplurality of tolerances independently based on one or more monitoredfactors.
 9. The computer-implemented method as recited in claim 1,further comprising: automatically approving the VM request provisionallysubject to a subsequent manual review by an IT administrator. 10-20.(canceled)